{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "讲师： 沈福利\n",
    "\n",
    "本章节目标\n",
    "1. 绘制一个简单的图形热力图\n",
    "2. 航班数据乘客数热力图\n",
    "3. 鸢尾花特征关联分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 热力图\n",
    "热力图，英文叫 heat map，是一种矩阵表示方法，其中矩阵中的元素值用颜色来代表，不同的颜色代表不同大小的值。通过颜色就能直观地知道某个位置上数值的大小。另外你也可以将这个位置上的颜色，与数据集中的其他位置颜色进行比较。\n",
    "热力图是一种非常直观的多元变量分析方法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# 设置字体大小和图表的大小\n",
    "plt.rc('figure',figsize = (16,10))\n",
    "plt.rc('font',size = 15)\n",
    "\n",
    "# 中文乱码问题\n",
    "from matplotlib.font_manager import _rebuild\n",
    "_rebuild()\n",
    "plt.rcParams['font.sans-serif']=[u'SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 绘制一个简单热力图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[41, 35, 51, 64, 16],\n",
       " [26, 16, 17, 37, 37],\n",
       " [46, 91, 77, 25, 38],\n",
       " [8, 78, 50, 37, 10],\n",
       " [79, 80, 11, 79, 22]]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#定义热图的横纵坐标\n",
    "xLabel = ['A','B','C','D','E']\n",
    "yLabel = ['1','2','3','4','5']\n",
    "\n",
    "#准备数据阶段，利用random生成二维数据（5*5）\n",
    "data = []\n",
    "for i in range(len(xLabel)):\n",
    "    temp = []\n",
    "    for j in range(len(yLabel)):\n",
    "        k = np.random.randint(0,100)\n",
    "        temp.append(k)\n",
    "    data.append(temp)\n",
    "    \n",
    "# 查看一下我们构建的数据   \n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'This is a heatmap title')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 作图阶段\n",
    "fig = plt.figure()\n",
    "# 定义一个画布1*1 个划分，并在第一个位置进行作图\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "# 定义x轴的坐标的刻度\n",
    "ax.set_yticks(range(len(yLabel)))\n",
    "ax.set_yticklabels(yLabel)\n",
    "\n",
    "ax.set_xticks(range(len(xLabel)))\n",
    "ax.set_xticklabels(xLabel)\n",
    "\n",
    "# data\n",
    "for i in range(len(xLabel)):\n",
    "    for j in range(len(yLabel)):\n",
    "        text = ax.text(j,i,data[i][j],ha='center',va = 'center',color='w')\n",
    "# 作图饼选择热图的颜色填充风格，这里选择hot\n",
    "im = ax.imshow(data,cmap=plt.cm.hot_r)\n",
    "# 增加右侧颜色列表\n",
    "plt.colorbar(im)\n",
    "# 增加title\n",
    "plt.title('This is a heatmap title')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 航班数据乘客数热力图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949</td>\n",
       "      <td>January</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949</td>\n",
       "      <td>February</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949</td>\n",
       "      <td>March</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949</td>\n",
       "      <td>April</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949</td>\n",
       "      <td>May</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     month  passengers\n",
       "0  1949   January         112\n",
       "1  1949  February         118\n",
       "2  1949     March         132\n",
       "3  1949     April         129\n",
       "4  1949       May         121"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flighs = pd.read_csv('data/flights.csv',index_col=0)\n",
    "flighs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "那么进一步分析flights 的数据，希望格式化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>January</th>\n",
       "      <th>February</th>\n",
       "      <th>March</th>\n",
       "      <th>April</th>\n",
       "      <th>May</th>\n",
       "      <th>June</th>\n",
       "      <th>July</th>\n",
       "      <th>August</th>\n",
       "      <th>September</th>\n",
       "      <th>October</th>\n",
       "      <th>November</th>\n",
       "      <th>December</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>129</td>\n",
       "      <td>148</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>112</td>\n",
       "      <td>148</td>\n",
       "      <td>135</td>\n",
       "      <td>132</td>\n",
       "      <td>121</td>\n",
       "      <td>104</td>\n",
       "      <td>119</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>135</td>\n",
       "      <td>170</td>\n",
       "      <td>140</td>\n",
       "      <td>126</td>\n",
       "      <td>115</td>\n",
       "      <td>170</td>\n",
       "      <td>149</td>\n",
       "      <td>141</td>\n",
       "      <td>125</td>\n",
       "      <td>114</td>\n",
       "      <td>133</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951</th>\n",
       "      <td>163</td>\n",
       "      <td>199</td>\n",
       "      <td>166</td>\n",
       "      <td>150</td>\n",
       "      <td>145</td>\n",
       "      <td>199</td>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>172</td>\n",
       "      <td>146</td>\n",
       "      <td>162</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>181</td>\n",
       "      <td>242</td>\n",
       "      <td>194</td>\n",
       "      <td>180</td>\n",
       "      <td>171</td>\n",
       "      <td>230</td>\n",
       "      <td>218</td>\n",
       "      <td>193</td>\n",
       "      <td>183</td>\n",
       "      <td>172</td>\n",
       "      <td>191</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>235</td>\n",
       "      <td>272</td>\n",
       "      <td>201</td>\n",
       "      <td>196</td>\n",
       "      <td>196</td>\n",
       "      <td>264</td>\n",
       "      <td>243</td>\n",
       "      <td>236</td>\n",
       "      <td>229</td>\n",
       "      <td>180</td>\n",
       "      <td>211</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      January  February  March  April  May  June  July  August  September  \\\n",
       "year                                                                        \n",
       "1949      129       148    118    118  112   148   135     132        121   \n",
       "1950      135       170    140    126  115   170   149     141        125   \n",
       "1951      163       199    166    150  145   199   178     178        172   \n",
       "1952      181       242    194    180  171   230   218     193        183   \n",
       "1953      235       272    201    196  196   264   243     236        229   \n",
       "\n",
       "      October  November  December  \n",
       "year                               \n",
       "1949      104       119       136  \n",
       "1950      114       133       158  \n",
       "1951      146       162       184  \n",
       "1952      172       191       209  \n",
       "1953      180       211       237  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = flighs.pivot('year','month','passengers')\n",
    "f.columns = ['January','February', 'March','April','May','June', 'July', 'August',  \n",
    "  'September','October', 'November',  'December']\n",
    "f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']\n"
     ]
    }
   ],
   "source": [
    "print(list(f.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960]\n"
     ]
    }
   ],
   "source": [
    "print(list(f.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "heatmap ： x 表示月份;y 年份 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[129, 148, 118, 118, 112, 148, 135, 132, 121, 104, 119, 136],\n",
       " [135, 170, 140, 126, 115, 170, 149, 141, 125, 114, 133, 158],\n",
       " [163, 199, 166, 150, 145, 199, 178, 178, 172, 146, 162, 184],\n",
       " [181, 242, 194, 180, 171, 230, 218, 193, 183, 172, 191, 209],\n",
       " [235, 272, 201, 196, 196, 264, 243, 236, 229, 180, 211, 237],\n",
       " [227, 293, 229, 188, 204, 302, 264, 235, 234, 203, 229, 259],\n",
       " [269, 347, 278, 233, 242, 364, 315, 267, 270, 237, 274, 312],\n",
       " [313, 405, 306, 277, 284, 413, 374, 317, 318, 271, 306, 355],\n",
       " [348, 467, 336, 301, 315, 465, 422, 356, 355, 305, 347, 404],\n",
       " [348, 505, 337, 318, 340, 491, 435, 362, 363, 310, 359, 404],\n",
       " [396, 559, 405, 342, 360, 548, 472, 406, 420, 362, 407, 463],\n",
       " [461, 606, 432, 391, 417, 622, 535, 419, 472, 390, 461, 508]]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xLabel = list(f.columns)\n",
    "yLabel = list(f.index)\n",
    "\n",
    "data = []\n",
    "for year in f.index:\n",
    "    data_year = f.loc[year].values\n",
    "    data.append(list(data_year))\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x11eb0bb00>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 作图\n",
    "fig,ax = plt.subplots()\n",
    "#ax = fig.add_subplot(111)\n",
    "# 定义x轴的坐标的刻度\n",
    "ax.set_yticks(range(len(yLabel)))\n",
    "ax.set_yticklabels(yLabel)\n",
    "\n",
    "ax.set_xticks(range(len(xLabel)))\n",
    "ax.set_xticklabels(xLabel)\n",
    "\n",
    "# x 轴标签设置\n",
    "for tick in ax.get_xticklabels():\n",
    "    tick.set_rotation(35)\n",
    "\n",
    "# data\n",
    "for i in range(len(xLabel)):\n",
    "    for j in range(len(yLabel)):\n",
    "        text = ax.text(j,i,data[i][j],ha='center',va = 'center',color='w')\n",
    "# 作图饼选择热图的颜色填充风格，这里选择hot\n",
    "im = ax.imshow(data,cmap=plt.cm.hot_r)\n",
    "# 增加右侧颜色列表\n",
    "plt.colorbar(im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 鸢尾花特征关联分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "species  =  ['setosa' 'versicolor' 'virginica']\n",
      "{'setosa': 0, 'versicolor': 1, 'virginica': 2}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width  species\n",
       "0           5.1          3.5           1.4          0.2        0\n",
       "1           4.9          3.0           1.4          0.2        0\n",
       "2           4.7          3.2           1.3          0.2        0\n",
       "3           4.6          3.1           1.5          0.2        0\n",
       "4           5.0          3.6           1.4          0.2        0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = pd.read_csv('data/iris.csv')\n",
    "print('species  = ',np.unique(iris['species']))\n",
    "# 定义label －index 的映射关系\n",
    "species_dict = {'setosa':0, 'versicolor':1, 'virginica':2}\n",
    "print(species_dict)\n",
    "\n",
    "def get_index(name):\n",
    "    return species_dict[name]\n",
    "\n",
    "iris_species = iris['species'].apply(get_index)\n",
    "iris['species'] = iris_species\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.117570</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817941</td>\n",
       "      <td>0.782561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>-0.117570</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>-0.366126</td>\n",
       "      <td>-0.426658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962865</td>\n",
       "      <td>0.949035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.817941</td>\n",
       "      <td>-0.366126</td>\n",
       "      <td>0.962865</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.956547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <td>0.782561</td>\n",
       "      <td>-0.426658</td>\n",
       "      <td>0.949035</td>\n",
       "      <td>0.956547</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width   species\n",
       "sepal_length      1.000000    -0.117570      0.871754     0.817941  0.782561\n",
       "sepal_width      -0.117570     1.000000     -0.428440    -0.366126 -0.426658\n",
       "petal_length      0.871754    -0.428440      1.000000     0.962865  0.949035\n",
       "petal_width       0.817941    -0.366126      0.962865     1.000000  0.956547\n",
       "species           0.782561    -0.426658      0.949035     0.956547  1.000000"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征分析\n",
    "corrmat = iris.corr()\n",
    "corrmat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = list(corrmat.columns)\n",
    "y = x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']\n",
      "['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']\n"
     ]
    }
   ],
   "source": [
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x11f35d240>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 作图阶段\n",
    "fig,ax = plt.subplots()\n",
    "# 定义刻度\n",
    "ax.set_yticks(range(len(y)))\n",
    "ax.set_yticklabels(y)\n",
    "\n",
    "ax.set_xticks(range(len(x)))\n",
    "ax.set_xticklabels(x)\n",
    "\n",
    "for tick in ax.get_xticklabels():\n",
    "    tick.set_rotation(35)\n",
    "\n",
    "# heatmap图\n",
    "im = ax.imshow(corrmat)\n",
    "plt.colorbar(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "193px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
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